Canonical Output Conditioning: Decoupling Navigation Interfaces from Diffusion Priors

A diffusion prior and the interface through which external conditions enter it are usually learned together: condition encoders, control branches, or classifier-free channels bind the trained model to the formats seen during training. Training-free guidance avoids retraining, yet still requires a compatibility function that relates each condition to the generated variable, typically a measurement operator, a differentiable loss, or a separately trained predictor. When condition and output live in different coordinates, constructing that function becomes its own learned component. We argue that this gap is fixed by a design choice made before either training or guidance: the choice of what the model generates. Canonical-output conditioning selects a representation whose coordinates both support the downstream task and admit direct comparison with externally converted conditions. The prior is then trained without conditions; heterogeneous formats reduce to explicit geometric adapters that feed a shared quadratic compatibility interface. We validate the design in route-guided navigation, where conditions are naturally heterogeneous. A LiDAR-conditioned diffusion prior over orientation fields, trained with no route inputs, is guided at inference by waypoint, corridor, and map route cues through Orientation Mixing, a confidence-weighted clean-estimate update. On six SemanticKITTI sequences, the single frozen prior serves all four interfaces and outperforms route-conditioned predictors trained on a fixed interface (mean ADE 0.29,m vs. 0.38,m), supporting representation choice as a practical lever for decoupling condition interfaces from diffusion priors.